Polymerase Chain Reaction (PCR) instrumentation has made it possible to perform reliable quantification of DNA or RNA levels in biological samples. Commercially available PCR instruments, and related data acquisition and analysis software, process qPCR assay data generated from biological samples. These systems report quantitative results by calculating a quantification cycle (Cq, CT or CRT) value as the fractional PCR cycle number where the reporter signal rises above a threshold set manually by a human or automatically by software. The determined Cq value can be used to estimate the initial quantity of DNA material.
In contrast to qPCR, a Digital PCR (dPCR) result set often requires analysis of several thousand PCR reactions. Generally, increasing the number of replicates increases the accuracy and reproducibility of dPCR results.
Digital Polymerase Chain Reaction (dPCR) is a method that has been described, for example, in U.S. Pat. No. 6,143,496 to Brown et al. Results from dPCR can be used to detect and quantify the concentration of rare alleles, to provide absolute quantitation of nucleic acid samples, and to measure low fold-changes in nucleic acid concentration.
One example of implementation of dPCR is often performed using apparatus adapted from conventional qPCR, in which replicates are arrayed in a two dimensional array format including m rows by n columns, i.e., an m×n format. PCR cycling and read-out (end-point or real-time) generally occurs within the same array. A maximum of m×n replicates can be processed in a single batch run.
The (m×n) format in most quantitative polymerase chain reaction (qPCR) platforms is designed for sample-by-assay experiments, in which PCR results need to be addressable for post-run analysis. For dPCR, however, the specific position or well of each PCR result may be immaterial and only the number of positive and negative replicates per sample may be analyzed.
The read-out of dPCR, that is, the number of positive reactions and the number of negative reactions, is linearly proportional to the template concentration, while the read-out of qPCR (signal vs. cycle) is proportional to the log of the template concentration. For this reason, dPCR typically is constrained to a narrow dynamic range of template input. As a result of the log versus linear, the dPCR analysis offers better resolution for close fold changes than qPCR. Furthermore, the resolution remains approximately linear across the entire dynamic range that is supported.
However, to determine positive and negative counts for a dPCR result after thermal cycling, also known as endpoint reads, requires determining or setting a fluorescence threshold. Thus, it is a challenge to determine where a threshold should be set to accurately distinguish between negatives and positives because there may be no or low amplifications for each sample. Furthermore, it is also a challenge to determine whether a sample volume contains one copy or multiple copies, for example.